Wei Shengyu
GitHub
commit
9b4b7a67b7
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@ -0,0 +1,13 @@
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AlgoModule:
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- preprocess:
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- processor_type: data_processor
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processor_name: image_processor
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image_processors:
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- ResizeImage:
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size: [640, 640]
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interpolation: 2
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- NormalizeImage:
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scale: 0.00392157
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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- ToRGB
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@ -1,20 +1,13 @@
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from abc import ABC, abstractmethod
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from algo_mod import build_algo_mod
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from searcher import build_searcher
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from data_processor import build_data_processor
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from processor.algo_mod import predictors, searcher
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def build_processor(config):
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processor_type = config.get("processor_type")
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if processor_type == "algo_mod":
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return build_algo_mod(config)
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elif processor_type == "searcher":
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return build_searcher(config)
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elif processor_type == "data_processor":
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return build_data_processor(config)
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else:
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raise NotImplemented("processor_type {} not implemented.".format(processor_type))
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processor_mod = locals()[processor_type]
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processor_name = config.get("processor_name")
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return getattr(processor_mod, processor_name)
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class BaseProcessor(ABC):
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@ -1,7 +1,14 @@
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from .fake_cls import FakeClassifier
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from .. import BaseProcessor, build_processor
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def build_algo_mod(config):
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algo_name = config.get("algo_name")
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if algo_name == "fake_clas":
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return FakeClassifier(config)
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class AlgoMod(BaseProcessor):
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def __init__(self, config):
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self.pre_processor = build_processor(config["pre_processor"])
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self.predictor = build_processor(config["predictor"])
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self.post_processor = build_processor(config["post_processor"])
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def process(self, input_data):
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input_data = self.pre_processor(input_data)
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input_data = self.predictor(input_data)
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input_data = self.post_processor(input_data)
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return input_data
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@ -0,0 +1 @@
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from image_processor import ImageProcessor
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@ -1,4 +1,4 @@
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from .. import BaseProcessor
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from processor import BaseProcessor
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class BBoxCropper(BaseProcessor):
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@ -1,85 +1,93 @@
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from functools import partial
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import six
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import math
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import random
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import cv2
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import numpy as np
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import importlib
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from PIL import Image
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import paddle
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from utils import logger
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from processor import BaseProcessor
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class PreProcesser(object):
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class ImageProcessor(BaseProcessor):
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def __init__(self, config):
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"""Image PreProcesser
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self.processors = []
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for processor_config in config.get("image_processors"):
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name = list(processor_config)[0]
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param = {} if processor_config[name] is None else processor_config[name]
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op = locals()[name](**param)
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self.processors.append(op)
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Args:
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config (list): A list consisting of Dict object that describe an image processer operator.
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"""
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super().__init__()
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self.ops = self.create_ops(config)
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def process(self, input_data):
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image = input_data["input_image"]
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for processor in self.processors:
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if isinstance(processor, BaseProcessor):
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input_data["image"] = image
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input_data = processor.process(input_data)
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else:
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image = processor(image)
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return input_data
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def create_ops(self, config):
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if not isinstance(config, list):
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msg = "The preprocess config should be a list consisting of Dict object."
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logger.error(msg)
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raise Exception(msg)
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mod = importlib.import_module(__name__)
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ops = []
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for op_config in config:
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name = list(op_config)[0]
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param = {} if op_config[name] is None else op_config[name]
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op = getattr(mod, name)(**param)
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ops.append(op)
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return ops
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class GetShapeInfo(BaseProcessor):
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def __init__(self):
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pass
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def process(self, input_data):
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input_image = input_data["input_image"]
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image = input_data["image"]
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input_data['im_shape'] = np.array(input_image.shape[:2], dtype=np.float32)
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input_data['input_shape'] = np.array(image.shape[:2], dtype=np.float32)
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input_data['scale_factor'] = np.array([image.shape[0] / input_image.shape[0],
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image.shape[1] / input_image.shape[1]], dtype=np.float32)
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class ToTensor(BaseProcessor):
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def __init__(self, config):
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pass
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def process(self, input_data):
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image = input_data["image"]
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input_data["input_tensor"] = paddle.to_tensor(image)
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return input_data
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class ToRGB:
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def __init__(self):
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pass
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def __call__(self, img):
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img = img[:, :, ::-1]
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return img
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class ToCHWImage:
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def __init__(self):
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pass
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def __call__(self, img, img_info=None):
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if img_info:
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for op in self.ops:
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img, img_info = op(img, img_info)
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return img, img_info
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img = img.transpose((2, 0, 1))
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return img
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class ResizeImage:
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def __init__(self,
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size=None,
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resize_short=None,
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interpolation=None,
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backend="cv2"):
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if resize_short is not None and resize_short > 0:
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self.resize_short = resize_short
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self.w = None
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self.h = None
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elif size is not None:
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self.resize_short = None
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self.w = size if type(size) is int else size[0]
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self.h = size if type(size) is int else size[1]
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else:
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for op in self.ops:
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img = op(img)
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return img
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raise Exception("invalid params for ReisizeImage for '\
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'both 'size' and 'resize_short' are None")
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class DecodeImage(object):
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""" decode image """
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def __init__(self, to_rgb=True, to_np=False, channel_first=False):
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self.to_rgb = to_rgb
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self.to_np = to_np # to numpy
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self.channel_first = channel_first # only enabled when to_np is True
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def __call__(self, img, img_info=None):
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if six.PY2:
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assert type(img) is str and len(
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img) > 0, "invalid input 'img' in DecodeImage"
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else:
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assert type(img) is bytes and len(
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img) > 0, "invalid input 'img' in DecodeImage"
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data = np.frombuffer(img, dtype='uint8')
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img = cv2.imdecode(data, 1)
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if self.to_rgb:
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assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
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img.shape)
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img = img[:, :, ::-1]
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if self.channel_first:
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img = img.transpose((2, 0, 1))
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if img_info:
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img_info["im_shape"] = np.array(img.shape[:2], dtype=np.float32)
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img_info["scale_factor"] = np.array([1., 1.], dtype=np.float32)
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return img, img_info
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else:
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return img
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class UnifiedResize(object):
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def __init__(self, interpolation=None, backend="cv2"):
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_cv2_interp_from_str = {
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'nearest': cv2.INTER_NEAREST,
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'bilinear': cv2.INTER_LINEAR,
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@ -114,38 +122,12 @@ class UnifiedResize(object):
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self.resize_func = partial(_pil_resize, resample=interpolation)
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else:
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logger.warning(
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f"The backend of Resize only support \"cv2\" or \"PIL\". \"f{backend}\" is unavailable. Use \"cv2\" instead."
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f"The backend of Resize only support \"cv2\" or \"PIL\". \"f{backend}\" is unavailable. "
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f"Use \"cv2\" instead."
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)
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self.resize_func = cv2.resize
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def __call__(self, src, size):
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return self.resize_func(src, size)
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class ResizeImage(object):
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""" resize image """
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def __init__(self,
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size=None,
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resize_short=None,
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interpolation=None,
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backend="cv2"):
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if resize_short is not None and resize_short > 0:
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self.resize_short = resize_short
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self.w = None
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self.h = None
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elif size is not None:
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self.resize_short = None
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self.w = size if type(size) is int else size[0]
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self.h = size if type(size) is int else size[1]
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else:
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raise Exception("invalid params for ReisizeImage for '\
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'both 'size' and 'resize_short' are None")
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self._resize_func = UnifiedResize(
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interpolation=interpolation, backend=backend)
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def __call__(self, img, img_info=None):
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def __call__(self, img):
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img_h, img_w = img.shape[:2]
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if self.resize_short is not None:
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percent = float(self.resize_short) / min(img_w, img_h)
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@ -154,17 +136,11 @@ class ResizeImage(object):
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else:
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w = self.w
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h = self.h
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img = self._resize_func(img, (w, h))
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if img_info:
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img_info["input_shape"] = img.shape[:2]
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img_info["scale_factor"] = np.array(
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[img.shape[0] / img_h, img.shape[1] / img_w]).astype("float32")
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return img, img_info
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else:
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return img
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img = self.resize_func(img, (w, h))
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return img
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class CropImage(object):
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class CropImage:
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""" crop image """
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def __init__(self, size):
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@ -173,34 +149,25 @@ class CropImage(object):
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else:
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self.size = size # (h, w)
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def __call__(self, img, img_info=None):
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def __call__(self, img):
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w, h = self.size
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img_h, img_w = img.shape[:2]
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if img_h < h or img_w < w:
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raise Exception(
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f"The size({h}, {w}) of CropImage must be greater than size({img_h}, {img_w}) of image. Please check image original size and size of ResizeImage if used."
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f"The size({h}, {w}) of CropImage must be greater than size({img_h}, {img_w}) of image. "
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f"Please check image original size and size of ResizeImage if used."
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)
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w_start = (img_w - w) // 2
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h_start = (img_h - h) // 2
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w_end = w_start + w
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h_end = h_start + h
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img = img[h_start:h_end, w_start:w_end, :]
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if img_info:
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img_info["input_shape"] = img.shape[:2]
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# TODO(gaotingquan): im_shape is needed to update?
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img_info["im_shape"] = np.array(img.shape[:2], dtype=np.float32)
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return img, img_info
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else:
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return img
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return img
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class NormalizeImage(object):
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""" normalize image such as substract mean, divide std
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"""
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class NormalizeImage:
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def __init__(self,
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scale=None,
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mean=None,
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@ -210,9 +177,8 @@ class NormalizeImage(object):
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channel_num=3):
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if isinstance(scale, str):
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scale = eval(scale)
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assert channel_num in [
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3, 4
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], "channel number of input image should be set to 3 or 4."
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assert channel_num in [3, 4], \
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"channel number of input image should be set to 3 or 4."
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self.channel_num = channel_num
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self.output_dtype = 'float16' if output_fp16 else 'float32'
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self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
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@ -224,12 +190,8 @@ class NormalizeImage(object):
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self.mean = np.array(mean).reshape(shape).astype('float32')
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self.std = np.array(std).reshape(shape).astype('float32')
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def __call__(self, img, img_info=None):
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if isinstance(img, Image.Image):
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img = np.array(img)
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assert isinstance(img,
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np.ndarray), "invalid input 'img' in NormalizeImage"
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def __call__(self, img):
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assert isinstance(img, np.ndarray), "invalid input 'img' in NormalizeImage"
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img = (img.astype('float32') * self.scale - self.mean) / self.std
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@ -244,25 +206,4 @@ class NormalizeImage(object):
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if self.order == 'chw' else np.concatenate(
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(img, pad_zeros), axis=2))
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img = img.astype(self.output_dtype)
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if img_info:
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return img, img_info
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else:
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return img
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class ToCHWImage(object):
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""" convert hwc image to chw image
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"""
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def __init__(self):
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pass
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def __call__(self, img, img_info=None):
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if isinstance(img, Image.Image):
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img = np.array(img)
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img = img.transpose((2, 0, 1))
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if img_info:
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return img, img_info
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else:
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return img
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return img
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@ -0,0 +1,7 @@
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from .fake_cls import FakeClassifier
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def build_algo_mod(config):
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algo_name = config.get("algo_name")
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if algo_name == "fake_clas":
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return FakeClassifier(config)
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@ -1,4 +1,4 @@
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from .. import BaseProcessor
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from processor import BaseProcessor
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class FakeClassifier(BaseProcessor):
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@ -1,5 +0,0 @@
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# from bbox_cropper import
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def build_data_processor(config):
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return
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@ -1,9 +0,0 @@
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from .. import BaseProcessor
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class ImageReader(BaseProcessor):
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def __init__(self):
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pass
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def process(self, input_data):
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pass
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